Parking is a key component of any transportation system. Parking needs must be address between businesses, residences and government entities in a collaborative manner. Parking is loaded with frustration for all parties involved. Any parking situation is a complex combination of problems such as the uncoordinated usage of either a single parking structure, multiple parking structures or on-street parking or both, the lack of timely and accurate information for motorists, hidden or unknown parking spaces, parking space occupancy, parking space categories, illegally (hazardously) parked cars, inefficient use of parking capacity, lack of dynamics to meet changing needs, inconvenient pricing methods, spillover accommodation, poor lighting, excessive energy usage, handicap spaces, expansion etc. Most of these problems are very hardware centric and expensive to remedy. Others require even more costly infrastructure changes to support any existing or new solution. Finally when a driver enters parking structures they are further inhibited because there is no way to access any parking solution functions.
One way to provide novel inexpensive solutions to parking problems is to include the use of cooperative lighting along with access to parking structure customer management functions and derived information from video sensors (and other sensors) within the parking facility. Lighting is always important because it influences the perception of security and safety in a facility. So you can pretty much count on lighting being present at parking facilities. Converting the lighting to assist with driver guidance is a start. Also using the existing lighting infrastructure in combination with video sensors (and other sensors) can alleviate many of the parking problems frustrating local government, motorists and parking facility owners.
Increasingly we see simple scalar sensors being utilized to bring some relief. But parking structures that employ these sensors find they are usually locked into particular solutions because sensors are expensive to deploy and typically tied one-to-one to each individual parking slot. They are also hard to install, not flexible, unintelligent and not easily re-purposed as needs change.
Another ongoing problem with deploying sensors, in general, is the appetite for information and the usage of communication facilities is always growing as parking structures evolve to become “smart”. This is because sensor technology is evolving from reporting simple scalar counts to more complex classification technology while feeding into smart parking management systems (for smart cities) as part of the expanding role of the Internet of Things (IoT) in everyday lives. Reporting activities in parking structures (such as counting vehicles, people in vehicles, types of vehicles etc.) is adding to the class of “killer applications” that will greatly assist in driving IoT deployments.
The ability to monitor for events and count activities is at the core of parking management. The dynamic nature of parking structures due to random driver behavior makes this monitoring and counting problem difficult. The constant collection of real time meta data regarding the total number of parking spaces at a facility, the number of handicapped and EV spaces, illegally parked vehicles etc. and the total occupancy of each category is key to achieving the most efficient parking structure management. Developing increasing dynamic aspects of parking structure management that is able to not only adapt lighting for energy savings and driver guidance, but to also reconfigure metadata collection as well as create parking space assignments on the fly, for example to create new handicapped spaces on demand etc., will be the next step in this evolution.
Metadata counts (a set of data that describes and gives information about other data) from simple sensors today are being expanded to provide live video streams. It's amazing what a camera can do when joined up with sensors and sophisticated software. Due to Moore's Law, processing has become very inexpensive compared to the cost of massive sensor and video transmission infrastructures supporting a large and growing number of sensor and video feeds. The key problem is to mine the information embedded into the video data and intelligently combine it with other sensor data. This allows the generation of much more efficient and compact metadata and not needlessly move raw video data around an energy and capacity constrained network. Instead, because of economic trends it is possible to attach programmable processors to the data collection points to reduce data volumes and manage the energy needed to power the network sensors/nodes to provide the highest monitoring coverage and assistance to parking space seeking drivers.
Correlating video and sensor data at the network edge to assist with parking issues is also an unsolved problem. Data collected by today's rudimentary sensor networks have simple scalar forms with minimal information which makes processing simple (typically simple calculations of addition, subtraction, division, sums, and averages for example are generated). It is difficult to form a comprehensive understanding of a dynamic environment based on simple scalar information. On the other hand, videos and images collected by video sensor networks are rich information but have complicated forms. They are sometimes compressed and sent to back-end servers to be processed (formatted, integrated, and analyzed.) to meet diverse application requirements.
Increasingly, new applications cannot be supported by typical scalar sensor networks because they require vast amounts of information which can only be obtained from an image or video. This is also true for the evolution of parking management solutions. Scalar data is insufficient for many applications such as combining video surveillance, traffic monitoring and smart parking management.
Using the installed power lines to transmit data in a parking structure avoids the difficulty and costs of running separate data connections or video feeds throughout the concrete and steel structure. But, transmitting data over noisy low speed power lines is difficult. The main problem is how to represent uncompressed, live stream video data over any communication media, such as power lines, operated as low bandwidth communication links. Other examples of low bandwidth communication links experiencing the problem of how to represent uncompressed, live stream video data, include twisted pair, coax cable, Ethernet, radio media such s WiFi, Bluetooth, cellular, IEEE 802.15.4 RF at longer distances, and ultra-narrow band/Low Power Wide Area Network communications like LoRa. Also, any noise or interference-impaired communications medium will run slowly as it contends with resulting transmission errors. Bandwidth usage is also a problem for most forms of communication media in situations where many sensors and video units may be communicating at the same time.